Improving the Accuracy of Predictive 2D-LC Optimization Strategies: Incorporation of Simulated Elution Profiles to...

Method development in online comprehensive two-dimensional liquid chromatography (LC × LC) requires the selection of a large number of experimental parameters. The complexity of this process has led to several computer-based LC × LC optimization algorithms being developed to facilitate LC × LC method development. One particularly relevant challenge for predictive optimization software is to accurately model the effect of second dimension (²D) injection band broadening under sample solvent mismatch and/or sample volume overload conditions. We report a novel methodology that combines a chromatographic numerical simulation model capable of predicting elution profiles of analytes under conditions where peak distortion occurs with a predictive multiparameter Pareto optimization approach for online LC × LC. Preliminary method optimization is performed using a theoretical model to predict ²D injection profiles, and optimal experimental configurations obtained from the Pareto fronts are then subjected to further optimization using the simulation model. This approach drastically reduces the number of simulations and therefore the computational demand. We show that the optimal experimental conditions obtained in this manner are similar to those obtained using a complete optimization using only the simulation model. Online HILIC × RP-LC separation of phenolic compounds was used to compare experimental data to simulated two- and three-dimensional contour plots. The main advantage of the proposed approach is the ability to predict the formation of split or deformed peaks in the ²D, a significant benefit in online LC × LC method optimization, especially for separation combinations with mismatched mobile phases. A further benefit is that simulated elution profiles can be used for the visualization of predicted two-dimensional chromatograms for method selection.